Top Remote Analysis Reporting Jobs in Portland, ME

The Clinical Data Manager will manage research databases, oversee EHR to EDC automation, ensure data quality, and perform statistical analyses.

The CX Automation Performance Analyst manages post-launch AI programs, ensuring ongoing performance and optimization, building dashboards, and maintaining documentation.

The Senior Data Engineer will scale and evolve the data platform, develop ETL/ELT pipelines, establish best practices, and optimize systems for performance while collaborating with various teams.

Implement and maintain ETL processes to ingest and transform hospital data into Meduit platforms. Build data models, ensure data integrity, document and track tasks in Jira, collaborate with cross-functional teams, support revenue-cycle data implementations, and mentor team members on best practices.

Design, implement, and optimize AWS-based data infrastructure and automated ETL/ELT pipelines using Airflow, DMS, Glue, Redshift, and Athena. Build Terraform modules and CI/CD for data workflows, enforce security/IAM, implement observability, optimize performance/cost, and mentor junior engineers.

Own and improve dbt models, ELT pipelines, and warehouse architecture (Snowflake or equivalent) to deliver reliable executive KPIs and metric governance. Build data spine for Finance/Operations, enforce data quality/testing, partner cross-functionally with Engineering, FP&A, and Accounting, and influence analytics engineering standards, tooling, and roadmap.

The Senior Customer Lifecycle Analyst will enhance marketing performance by leveraging technical expertise in customer lifecycle management, evaluating campaign effectiveness, and providing strategic insights to optimize marketing strategies.

As a Data Analyst, you will create dashboards, analyze data, and ensure data quality while working with the VP of Finance.

Lead fraud detection and mitigation for a conversational BNPL product. Define fraud typologies, own repayment and chargeback analytics, manage fraud KPIs, design operational policies, collaborate on ML models and decisioning workflows, and coordinate SARs and compliance reporting.

Design and maintain enterprise data architecture and lakehouse solutions. Lead data migration, integration, governance, and BI enablement. Define strategy, implement pipelines, ensure data quality, security, and provide technical leadership and mentoring.